Training program customization using sensor-equipped athletic garments

ABSTRACT

An exercise feedback system monitors the performance of athletes wearing a garment with sensors while exercising. The sensors generate physiological data such as muscle activation data, heart rate data, or data describing the athlete&#39;s movement. The system extracts features from the physiological data and compares the features with reference exercise data to determine metrics of performance and biofeedback. Based on the physiological data, the system may also modify exercise training programs for the athlete. The exercise feedback system can display the biofeedback using visuals or audio, as well as modified exercise training programs, via the athlete&#39;s client device in real time while the athlete is exercising. By reviewing the biofeedback, the athlete may correct the athlete&#39;s exercise form to properly use the target muscles for the exercise, or change the certain workouts to personalize the training program.

BACKGROUND 1. Field of Art

This description generally relates to sensor-equipped athletic garments,and specifically to detecting athletic performance using sensor-equippedathletic garments and modifying an exercise training program inresponse.

2. Description of the Related Art

Sensors record a variety of information about the human body. Forexample, electrocardiograph (ECG) electrodes can measure electricalsignals from the skin of a person that are used to determine theperson's heart rate. In addition, electromyography (EMG) electrodes canmeasure electrical activity generated by a person's muscles. Heart rateand muscle movement information may be useful for evaluating theperson's physiological condition, for instance, while exercising. Thisinformation may also be used to evaluate the performance of an athleteduring strength and conditioning training.

When exercising, athletes and coaches may not be able to determinewhether the athlete is properly performing certain types of exercises.For example, a bench press exercise has a proper form that requires anathlete to focus on exerting a particular set of muscles in the upperbody. Performing exercises with improper form results in suboptimalexercise training for athletes, and may even cause injury to an athlete.Also, without proper form, the athlete may not be gaining the intendedbenefit from an exercise (e.g., strengthening a specific muscle grouptargeted by the exercise). Additionally, athletes may not recognize whenthey reach a level of fatigue that is negatively impacting theirexercise performance. Currently, an athlete can work with a coach whoobserves the athlete's performance and provides feedback. However, itmay not be practical for an athlete to exercise with a coach at alltimes. Further, feedback provided by coaches can be subjective, based onhow the athlete feels at a given time, or a rough observation by thehuman eye of the motion of the athlete.

SUMMARY

An exercise feedback system builds exercise training programs forathletes. Athletes wear a garment with sensors while exercising. Thesensors generate physiological data such as muscle activation data,heart rate data, or data describing the athlete's movement. Based on thephysiological data for an athlete, the exercise feedback system modifiesan exercise training program for the athlete. For example, if thephysiological data indicates that the athlete is performing a squatexercise with improper balance (leaning too much on the left or rightside), the exercise feedback system adds exercises that help improve theuser's balance to the exercise training program. The exercise feedbacksystem displays the modified exercise training program on the athlete'smobile device so that the athlete can change the athlete's exerciseworkouts. Incorporating physiological data, muscle usage data, exertiondata, timing data, and/or fatigue data into the process of executing atraining program personalizes the athlete's training to improve trainingresults.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram of a system environment for monitoring exercise dataaccording to one embodiment.

FIG. 2 is a diagram of a sensor-equipped athletic garment according toone embodiment.

FIG. 3 is a diagram of an exercise plan model used by the exercisefeedback system according to one embodiment.

FIG. 4A is a block diagram of an exercise feedback system according toone embodiment.

FIG. 4B is a block diagram of a client device according to oneembodiment.

FIG. 5A is a diagram of an athlete performing a squat exercise whilewearing a sensor-equipped garment according to one embodiment.

FIG. 5B is a user interface showing muscle activation feedback accordingto one embodiment.

FIG. 5C is a user interface showing exercise balance feedback accordingto one embodiment.

FIG. 5D is a user interface showing exercise set score feedbackaccording to one embodiment.

FIG. 5E is a user interface showing an exercise training programaccording to one embodiment.

FIG. 5F is a user interface showing a modified version of the exercisetraining program shown in FIG. 5E according to one embodiment.

FIG. 6A is a diagram of an athlete performing a bench press exercisewhile wearing a sensor-equipped garment according to one embodiment.

FIG. 6B is a user interface showing muscle activation feedback accordingto one embodiment.

FIG. 6C is a user interface showing target muscle feedback according toone embodiment.

FIG. 6D is a user interface showing exercise set score feedbackaccording to one embodiment.

FIG. 6E is a user interface showing an exercise training programaccording to one embodiment.

FIG. 6F is a user interface showing a modified version of the exercisetraining program shown in FIG. 6E according to one embodiment.

FIG. 7 is a flowchart of a process for providing exercise feedbackaccording to one embodiment.

FIG. 8 is a flowchart of a process for modifying an exercise trainingprogram according to one embodiment.

FIG. 9A is a user interface showing muscle activation feedback for upperbody muscles according to one embodiment.

FIG. 9B is a user interface showing muscle activation feedback for lowerbody muscles according to one embodiment.

FIG. 9C is another user interface showing muscle activation feedback forlower body muscles according to one embodiment.

FIG. 9D is a user interface showing muscle contribution over timeaccording to one embodiment.

The figures depict embodiments of the present invention for purposes ofillustration only. One skilled in the art will readily recognize fromthe following discussion that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles of the invention described herein.

DETAILED DESCRIPTION I. System Overview

FIG. 1 is a diagram of a system environment for monitoring exercise dataaccording to one embodiment. The system architecture includes anexercise feedback system 100, client device 110 (also referred to as an“athlete's device”), client device 120 (also referred to as a “coach'sdevice”), and athletic garment 130 communicatively coupled together viaa network 140. Users of the exercise feedback system 100 are alsoreferred to herein as “athletes.” In other embodiments, different and/oradditional entities can be included in the system architecture.

The client devices 110 and 120 are computing devices capable ofreceiving user input as well as transmitting and/or receiving data viathe network 140. A client device is a device having computerfunctionality, such as a smartphone, personal digital assistant (PDA), amobile telephone, tablet, laptop computer, desktop computer, or anothersuitable device. In one embodiment, a client device executes anapplication allowing a user of the client device to interact with theexercise feedback system 100. For example, a client device executes abrowser application to enable interaction between the client device andthe exercise feedback system 100 via the network 140. In anotherembodiment, a client device interacts with the exercise feedback system100 through an application programming interface (API) running on anative operating system of the client device, such as IOS® or ANDROID™.

The network 140 includes any combination of local area and/or wide areanetworks, including both wired and/or wireless communication systems. Inone embodiment, the network 140 uses standard communicationstechnologies and/or protocols. For example, the network 140 includescommunication links using technologies such as Ethernet, 802.11,worldwide interoperability for microwave access (WiMAX), 3G, 4G, codedivision multiple access (CDMA), digital subscriber line (DSL),BLUETOOTH®, Wi-Fi, ZIGBEE®, other suitable close-range networks, etc.Examples of networking protocols used for communicating via the network140 include multiprotocol label switching (MPLS), transmission controlprotocol/Internet protocol (TCP/IP), hypertext transport protocol(HTTP), simple mail transfer protocol (SMTP), and file transfer protocol(FTP). Data exchanged over the network 140 may be represented using anysuitable format, such as hypertext markup language (HTML) or extensiblemarkup language (XML). In some embodiments, all or some of thecommunication links of the network 140 may be encrypted using anysuitable technique or techniques.

An athlete 150 wears the athletic garment 130 while performingexercises. The athletic garment 130 records physiological data, e.g.,muscle activation data, heart rate data, or motion data, of the athlete.Based on the physiological data, the exercise feedback system 100generates exercise feedback personalized for the athlete. Further, acoach of the athlete can view the exercise feedback on the coach'sdevice 120 and provide additional feedback for the athlete. The athletecan view the exercise feedback and any additional feedback displayed ona user interface of the athlete's device 110.

II. Athletic Garment

FIG. 2 is a diagram of a sensor-equipped athletic garment 130 accordingto one embodiment. The athletic garment 130 includes sensors thatcontact the skin of an athlete wearing the athletic garment 130. Forexample, the sensors can be electrodes that measure electromyography(EMG) signals (electrical signals caused by muscle cells) also referredto as muscle activation data or electrocardiograph (ECG) signals(electrical signals caused by depolarization of the user's heart musclein particular) also referred to as heart rate data. The sensors may alsoinclude other types of sensors such as accelerometers and gyroscopes(which generate motion data based on the athlete's movement),temperature sensors, pressure sensors, humidity sensors, etc. Thesensors generate physiological data of the athlete based on the measuredsignals. The sensors are communicatively coupled to a processing unit290. The processing unit 290 can aggregate and analyze the physiologicaldata from the sensors. The processing unit 290 can also provide thephysiological data to the athlete's device 110, coach's device 120, orexercise feedback system 100 via the network 140.

In the embodiment shown in FIG. 2, the athletic garment 130 includeseight sensors that record muscle activation data from the athlete'smuscles nearby each sensor. In particular, sensors 210 and 220 locatedon the right and left shoulder of the athletic garment 130 can recordmuscle activation data of the athlete's deltoid muscles. Sensors 230 and240 located on the right and left sleeves of the athletic garment 130can record muscle activation data of the athlete's triceps and/or bicepmuscles. Sensors 250 and 260 located on the right and left chest of theathletic garment 130 can record muscle activation data of the athlete'spectoral muscles. Sensors 270 and 280 located on the right and leftabdomen of the athletic garment 130 can record muscle activation data ofthe athlete's abdominal and oblique muscles. Though the athletic garment130 shown in FIG. 2 includes eight sensors and the processing unit 290,in other embodiments, the athletic garment 130 can include any number ofsensors or other types of components or electronics at any location orconfiguration within the athletic garment 130.

It should be noted that while the athletic garment 130 shown in FIG. 2is a long sleeve shirt, the principles described herein apply equally toany garment, including but not limited to a short sleeved shirt, a tanktop, pants, shorts, or any other suitable garment. In embodiments wherethe athletic garment 130 is a pant, sensors of the athletic garment 130can record muscle activation data from muscles on an athlete's lowerbody, e.g., quadriceps (also referred to herein as “quad” or “quads”),gluteus maximus (also referred to herein as “glute” or “glutes”),hamstrings, calves, and the like.

III. Exercise Plan Model

FIG. 3 is a diagram of an exercise plan model used by the exercisefeedback system 100 according to one embodiment. The exercise plan modelis a hierarchal model with a goal, programs, workouts, exercises, andsets. The goal 300 is an objective that an athlete wants to work towardsor achieve. The goal can describe a general athletic characteristic,e.g., power, strength, hypertrophy, endurance, speed, or flexibility.Additionally, the goal can describe more specific athleticcharacteristics, e.g., vertical leap height, a long distance runningmetric, or arm dexterity. Goals can describe a training objective for aparticular sport, e.g., football or basketball, or player position for asport, e.g., lineman for football or point guard for basketball.Further, goals can describe other objectives that the athlete ispursuing, e.g., losing weight, gaining muscle mass, or toning armmuscles.

A goal is associated with one or more programs. An athlete can completeprograms, also referred to as exercise training programs, to helpachieve the corresponding goal. In the embodiment shown in FIG. 3, thegoal 300 is associated with the programs 310A and 310B. In one examplewhere the goal is strength, the programs can include “upper bodystrength,” “lower body strength,” and “core body strength.” In anotherexample where an athlete's goal is to train to play as a footballlineman, the programs can include “lateral movement,” “explosive power,”and “upper body strength,” e.g., because these programs focus onathletic skills that are useful for football linemen. In yet anotherexample where an athlete's goal is to train to play as a basketballpoint guard, the programs can include “lateral movement,” “basketballdribbling,” and “arm strength,” e.g., because these programs focus onathletic skills that are useful for basketball point guards. Thus, theprograms are customized for an athlete based on the athlete's specificgoals.

Each program is associated with one or more workouts. Workouts are setsof exercises that an athlete can complete consecutively or in oneactivity session as part of the corresponding program. In the embodimentshown in FIG. 3, the program 310A is associated with the workouts 320A,320B, and 320C. In one example where the program is “upper bodystrength,” the workouts can include “shoulder strength,” “upper backstrength,” and “chest strength,” e.g., because these workouts each helpdevelop strength in upper body muscles. In another example where theprogram is “lateral movement,” the workouts can include “lateral speed,”“lateral agility,” and “lateral explosiveness,” e.g., because theseworkouts each help develop different lateral movement skills.

Each workout is associated with one or more exercises. An athlete cancomplete exercises as part of the corresponding workout. In theembodiment shown in FIG. 3, the workout 320A is associated with theexercises 330A, 330B, 330C, and 330D. In one example where the workoutis “upper body strength,” the exercises can include “bench press” and“overhead squat”. In another example where the workout is “lateralspeed,” the exercises can include “lateral lunge” and “lateral shuffle.”

Each exercise is associated with one or more sets. An athlete cancomplete sets as part of the corresponding exercise. In the embodimentshown in FIG. 3, the exercise 330A is associated with the sets 340A,340B, 340C, and 340D. Depending on the type of a set, the set isassociated with at least one of a weight, a number of repetitions, adistance, or a duration in time for an athlete to perform the exercise.In one example where the exercise is “bench press,” the set indicatesthat the athlete should perform eight repetitions lifting 180 pounds perrepetition. In another example where the exercise is “lateral shuffle,”the set indicates that the athlete should perform a lateral shuffle for5 meters in both the left and right directions for 5 repetitions, orperform a lateral shuffle back and forth for 3 consecutive minutes.

IV. Exercise Feedback System

FIG. 4A is a block diagram of the exercise feedback system 100 accordingto one embodiment. The exercise feedback system 100 includes a dataprocessing module 400, biofeedback module 410, exercise program builder430, exercise data store 440, and athlete data store 450. In otherembodiments, the exercise feedback system 100 may include additional,fewer, or different components for various applications, which are notshown so as to not obscure the details of the system architecture.

The data processing module 400 processes physiological data generated bysensors of an athletic garment (e.g., athletic garment 130 shown in FIG.2). The exercise feedback system can receive the physiological data fromthe athlete's device 110 or the processing unit 290 of the athleticgarment 130. The data processing module 400 can process thephysiological data by performing noise filtering or feature extraction.Features can include a heart rate or level of muscle activation for acertain muscle of an athlete. For example, the data processing module400 determines the heart rate, e.g., in beats per minute, of an athletebased on parameters from electrocardiograph data. In another example,the data processing module 400 calculates the amount of time that theheart rate of the athlete was within a predetermined percentage range ofthe athlete's maximum heart rate. In another example, the dataprocessing module 400 determines a level of muscle activation for aparticular muscle of an athlete based on the muscle activation data,e.g., an average of the muscle activation data or a peak amplitude ofthe muscle activation data. The level of muscle activation can berepresented as “low,” “medium,” or “high,” a percentage value, oranother other suitable range of values.

In one embodiment, the data processing module 400 extracts features thatrepresent the comparative contribution of different muscle groups to anexercise. The activation of a muscle over time can be accumulated torepresent the energy or work expended by the muscle during the movement(e.g., of the exercise). The data processing module 400 may calculatethe percentage contribution of the muscle to the movement based on theratio of work calculated for a given muscle to the sum of workcalculated for all muscles measured.

Further, the data processing module 400 can extract temporal patternsfrom the physiological data. For example, the data processing module 400determines the time difference between a first muscle activation and asecond muscle activation, which may indicate how closely an athlete isperforming an exercise with proper form and whether the athlete is usingthe correct sequence of muscles. The data processing module 400 maydetermine sequencing and form of an athlete's performance of an exerciseby comparing the time difference between different events of muscleactivation data such as the start, end, or peak amplitude for eachmuscle. Additionally, the data processing module 400 determinestimestamps of an athlete's movements based on motion data, e.g., atimestamp corresponding to when the athlete started an exercise, endedan exercise, or performed a certain athletic movement such as a jump,sprint, lift of an arm, or specific phases of a movement such as thelowering or raising phase of a squat. The data processing module 400 canstore the extracted features in the athlete data store 450 along withinformation identifying the corresponding athlete.

The data processing module 400 can determine features based on acomputation of one or more other features of physiological data. Forexample, the data processing module 400 computes a ratio of a firstlevel of muscle activation (of an athlete's left biceps muscle) to asecond level of muscle activation (of an athlete's right biceps muscle),which can indicate the athlete's balance, form, or other types ofmetrics.

As another example, the data processing module 400 computes a level ofaerobic fatigue or endurance based on an athlete's heart rate and/or theduration of time spent within different percentage ranges of theathlete's maximum heart rate. Additionally, the data processing module400 may determine a level of anaerobic fatigue or endurance based on anaccumulation of muscle activation over a predetermined period of time,e.g., representing a rep, set, workout, or program. The accumulation ofmuscle activation over time represents the energy or work expended bythe muscle and may be aggregated across all muscle groups measured tocalculate an overall total work or load placed on the athlete's body.The data processing module 400 may use this information to predictathlete fatigue.

The biofeedback module 410 generates biofeedback for users of theexercise feedback system 100 based on features extracted by the dataprocessing module 400. The biofeedback indicates a metric of performance(e.g., satisfactory or unsatisfactory) of an athlete performingexercises. The biofeedback module 410 can store the biofeedback in theathlete data store 450 along with information identifying thecorresponding athlete. The biofeedback module 410 can compare theextracted features with features based on reference exercise data fromthe exercise data store 440. In one example, the reference exercise dataindicates a target range of heart rate (e.g., heart rate afterexercising or heart rate while performing high intensity exercises)based on demographic information of an athlete (e.g., age or gender). Ifthe athlete's heart rate indicated by the extracted features fallswithin the corresponding target range, then the biofeedback module 410generates biofeedback indicating that the athlete has a satisfactoryheart rate.

In another example, the reference exercise data indicates target muscleactivation levels based on a given type of exercise. For instance, for asquat exercise, the reference exercise data indicates that thequadriceps or glutes should fall within a given range of muscleactivation. Additionally, for a bench press exercise, the referenceexercise data indicates that the pectorals and deltoids should have ahigh level of muscle activation and that the triceps should have amedium to high level of muscle activation. The biofeedback module 410can compare the athlete's actual muscle activation to the target muscleactivation information. If the extracted features indicate that anathlete's muscle activation levels do not meet the target muscleactivation levels, biofeedback module 410 generates biofeedbackindicating that the athlete performed the exercise with anunsatisfactory effort. If the extracted features indicate that anathlete's muscle activation levels are not balanced betweencorresponding muscles (e.g., quadriceps in the left leg and quadricepsin the right leg), biofeedback module 410 generates biofeedbackindicating that the athlete has unsatisfactory balance. Further, thebiofeedback can indicate that the athlete is activating the incorrectmuscles for a particular exercise, e.g., the deltoids are activated morethan the pectorals or triceps during a bench press exercise.

In yet another example, the reference exercise data indicates baselinemotion profiles for various types of exercises. The baseline motionprofiles are based on motion data generated by sensors (e.g.,accelerometers or gyroscopes) worn by a reference athlete, e.g., anexpert that previously performed a given exercise. The baseline motionprofiles can include a first profile generated when the referenceathlete performed the given exercise using proper form and secondprofile generated when the reference athlete performed the givenexercise using an improper form. The biofeedback module 410 can comparethe athlete's actual motion profiles to the baseline motion profiles. Ifthe extracted features match features of the first profile, thebiofeedback module 410 generates biofeedback indicating that the athleteis performing the exercise using proper form. If the extracted featuresmatch features of the second profile, the biofeedback module 410generates biofeedback indicating aspects of the athlete's form thatdeviate from desired proper form (e.g., for a squat exercise, theathlete is not keeping their shins straight, sitting back, and pushingthrough their heels when raising out of the squat position). In additionto baseline motion profiles, the reference exercise data can alsoinclude baseline muscle activation data, timing data, fatigue data, orheart rate data of an expert while performing a particular exercise. Thebiofeedback module 410 can use any of the baseline data for comparisonwith the features extracted from the user's performance of an exercise.

In one embodiment, by leveraging muscle activation, timing, fatigue, orheart rate data across populations of different athletic skill, theexercise feedback system 100 determines targets associated with muscleactivation, timing, fatigue, or heart rate metrics to provide anunderstanding to the athlete regarding how a given metric should changeto demonstrate progression. For example, based on data of a populationof athletes that have well trained lower body posterior chains andproficiency in completing a deadlift movement, the exercise feedbacksystem 100 determines that the target (e.g., average) percentagecontribution of the glute and hamstring muscles (e.g., based on the workmetric) to the deadlift movement are approximately 40% and 30%,respectively. In an example use case, a given athlete is loading more oftheir quadriceps muscles, resulting in lower glute and hamstringcontributions, e.g., 25% and 20%, respectively. The exercise feedbacksystem 100 provides the given athlete with feedback to show their musclecontribution during the deadlift movement set-by-set to track progresstowards the target percentage contributions.

The biofeedback module 410 can generate biofeedback indicating a levelof fatigue of the athlete. For example, the athlete performs the firstbench press exercise of a set using proper form and performs the fifthbench press exercise of the set (e.g., a set of eight total exercises)using improper form. As the athlete fatigues, the athlete's quality ofmovement may suffer and the athlete deviates from the proper form. Usingthe bench press exercise as an example, as the athlete fatigues, if theathlete's chest and triceps muscle are weak, the athlete's deltoids maycompensate and thus have a much greater contribution during the fifthset as compared to the first set. The biofeedback module 410 may alertthe athlete about this change and provide biofeedback to correct theathlete's form. Further, the biofeedback module 410 may provide an alertto a client device of the athlete's coach. The biofeedback module 410can also determine the level of fatigue based on heart rate data andmuscle activation data.

The biofeedback module 410 can generate biofeedback indicating that theathlete violated one or more exercise rules while performing anexercise. The biofeedback module 410 retrieves exercise rules from theexercise data store 440. For example, an exercise rule indicates thatthe athlete should use the pectorals as the primary source of strengthand the triceps as a secondary source of strength when performing benchpress exercises. Exercise rules may be categorized based on a level ofpriority. For example, an exercise rule indicating that an athlete isusing improper form (e.g., exerting quad muscles too much whenperforming a deadlift exercise) is high priority, e.g., because failingto correct improper form could injure the athlete. In contrast, anexercise rule indicating that the user is slightly unbalanced whenperforming an exercise may have a lower priority. In some embodiments,the biofeedback module 410 generates biofeedback based on higherpriority exercise rules before generating biofeedback based on lowerpriority exercise rules.

The biofeedback module 410 can generate biofeedback for an athlete basedon the athlete's previously saved biofeedback in the athlete data store450 and based on performance trends determined from the savedbiofeedback. Thus, the biofeedback module 410 can compare the athlete'scurrent performance to past performances and determine performancetrends over a period of time (e.g., a week, month, or year). Forexample, the performance trends indicate that the athlete's form for asquat exercise is gradually becoming more similar to the target properform based on reference exercise data. As another example, theperformance trends indicate that the athlete is achieving satisfactorymetrics of performance for bench press exercises while increasing theamount of weight lifted per exercise by an average of five pounds permonth for the last six months. The biofeedback module 410 may compareperformance trends between sets within a given workout for a givenexercise, or across multiple workouts. The biofeedback module 410 mayalso compare overall workout level data, e.g., accumulated muscleactivation data over the workout. Based on the work metric, thebiofeedback module 410 can compare loading on different muscles betweenworkouts and evaluate if certain muscle groups are being over-trained orunder-trained with respect to other muscle groups.

The biofeedback module 410 can generate biofeedback for an athlete basedon information from a population of athletes of the exercise feedbacksystem 100, e.g., stored in the athlete data store 450. The biofeedbackmodule 410 can compare the athlete's performance with comparable otherathletes categorized by demographic data, geographic data, athleticskill level (e.g., amateur or professional), or other types of athletedata, e.g., one or more given sports played by athletes, or positionplayed by the athlete in the sport. For example, the biofeedback module410 generates biofeedback indicating that the athlete is lifting tenpounds more than the average weight lifted by other athletes who arealso males and in the same weight group, e.g., 150 to 180 pounds. Inanother example, the biofeedback indicates that the athlete's heart ratewhile performing a given cardio exercise is 10% lower on average thanthose other athletes while performing the given cardio exercise who arein a same age range, e.g., 20 to 30 years old. In another example, thebiofeedback module 410 compares the athlete's performance to moreproficient or advanced athletes to understand the difference and targetfor a given metric, e.g. decrease the contribution of the quads by 10%and increase the contribution of the glutes by 10%.

In one embodiment, the biofeedback module 410 generates a set scoreindicating a metric of performance of a set of exercises performed by anathlete. The biofeedback module 410 may generate the set score based onaggregate data of muscle effort, balance, and form. A high set score canindicate that the athlete is consistently achieving or exceedingsatisfactory metrics of performance for the set of exercises, e.g., byperforming exercises with proper form and muscle activation. On theother hand, a low set score can indicate that the athlete hasunsatisfactory metrics of performance throughout exercises in the set,e.g., by performing exercises with improper form and unbalanced muscleactivation. In one embodiment, the set score is a numerical valuebetween zero and ten. A high set score would be in the range of seven toten, a low set score would be in the range of zero to three, and amedium (or neutral) set score would be in the range of three to seven.In other embodiments, the set score can be represented in other forms,e.g., a percentage value, a value between 0 and 100, or a letter gradesuch as “A,” “B,” “C,” “B,” or “F.”

In one embodiment, the biofeedback module 410 generates set scores basedon target metrics associated with a given exercise, e.g., whether datavalues indicating the athlete's exertion level, balance, and form arewithin a target range of values associated with the given exercise. Theexertion level may be proportional to the athlete's muscle activationduring the given exercise. The balance for a particular muscle group isbased on whether the left and right muscles of the group haveapproximately the same muscle activation or exertion levels. The form isbased on whether the athlete is exerting the target muscles, and in atarget sequence, for the given exercise.

The exercise program builder 430 generates exercise training programs(e.g., corresponding to a program shown in FIG. 3) for athletes of theexercise feedback system 100. The exercise program builder 430 cangenerate an exercise training program based on a certain goal providedby an athlete, or can generate a set of predetermined exercise trainingprograms that athletes can choose from. The exercise training programcan include one or more workouts per day, scheduled over a duration oftime, e.g., a week, month, year, etc.

The exercise program builder 430 can modify exercise training programsover time based on biofeedback from the biofeedback module 410, inputinformation from an athlete received via the athlete's device 110, orinput information from a coach of the athlete received via the coach'sdevice 120. For example, the input information indicates that theathlete wants a more challenging exercise training program, so theexercise program builder 430 modifies exercise training programs toinclude more workouts, more sets of exercises, or exercises with greateramounts of weights. In another example, the input information indicatesthat the coach wants to reduce the number of workouts per week for anathlete because the coach views biofeedback indicating that the athleteis frequently becoming too fatigued during workouts. Thus, the programbuilder 430 modifies exercise training programs to include fewerworkouts, fewer sets per workout, or exercises with smaller amounts ofweights.

FIG. 4B is a block diagram of the client device 110 according to oneembodiment. The client device 110 includes an interface manager 460,exercise program module 470, local exercise data store 480, and localathlete data store 490. In other embodiments, the client device 110 mayinclude additional, fewer, or different components for variousapplications, which are not shown so as to not obscure the details ofthe system architecture. The client device 120 is substantially the sameas the client device 110, though as previously noted, the client device120 is used by a coach of the athlete using the client device 110.

In some embodiments, some or all of the functionality of the exercisefeedback system 100 may be performed by or implemented within the clientdevice 110. For example, the client device may include a biofeedbackmodule to generate biofeedback based on physiological data received fromthe athletic garment 130. This can be advantageous because the clientdevice 110 may not always have a network connection while an athlete isexercising (e.g., the athlete's gym does not have internet available).Thus, the biofeedback is generated locally on the client device 110without having to upload the physiological data to the exercise feedbacksystem 100 for processing.

The interface manager 460 receives physiological data from the athleticgarment 130 and can provide the physiological data to the exercisefeedback system 100 for further processing. The interface manager 460receives biofeedback, set scores, exercise training programs, and otherinformation from the exercise feedback system 100, e.g., referenceexercise data or extracted features from the data processing module 400.Based on the received information, the interface manager 460 generatesgraphical user interfaces (further described in Sections V, VI, and VIIIwith reference to FIGS. 5A-F, FIGS. 6A-F, and FIGS. 9A-D) depicting thebiofeedback, set scores, or exercise training programs. The interfacemanager 460 can store physiological data, biofeedback, set scores, orexercise training programs in the local athlete data store 490. Theinterface manager 460 stores the reference exercise data and extractedfeatures in the local exercise data store 480.

The interface manager 460 can receive athlete information input by theathlete via the client device 110. The interface manager 460 can storethe athlete information in the local athlete data store 490 or providethe athlete information to the exercise feedback system 100 to be storedin the athlete data store 450. The athlete information can describe,e.g., a goal of the athlete, demographic data (age or gender),geographical location, one or more sports that the athlete plays,history of injuries of the athlete, other types of data such asbiometrics including weight and height. Additionally, the interfacemanager 460 can receive information input by a coach of the athlete viathe client device 120, and provide the input information to the exercisefeedback system 100.

The exercise program module 470 can modify exercise training programsreceived from the exercise feedback system 100. Similar to the exerciseprogram builder 430, the exercise program module 470 modifies theexercise training programs based on physiological data, biofeedback, setscores, or input from athletes or coaches. However, the exercise programmodule 470 modifies the exercise training programs locally on theathlete's device 110 or coach's device 120. The exercise program module470 can provide the modified exercise training programs to the exercisefeedback system 100. In one example use case, the athlete provides inputto modify an exercise training program. The exercise program module 470modifies the exercise training program locally, but does not immediatelyprovide the modifications to the exercise feedback system 100 becausethe athlete's device 110 does not have a network connection. Theexercise program module 470 stores the modifications in the localathlete data store 490 and provides the modifications to the exercisefeedback system 100 at a later time when the athlete's device 110 has anetwork connection. Afterwards, the exercise feedback system 100 canalso provide the modified exercise training program to a coach's device120 for display to the athlete's coach.

V. Example Use Case: Squat

FIG. 5A is a diagram of an athlete 150 performing a squat exercise whilewearing a sensor-equipped garment 130 according to one embodiment. Theathlete 150 is wearing the sensor-equipped athletic garment 130 (a pairof shorts) including sensors that generate muscle activation data aboutthe athlete's lower body muscles, e.g., quadriceps and glutes. Theathlete 150 may be performing a set of the squat exercise with abarbell, e.g., a set of ten repetitions with 45 pounds of weight on thebarbell per repetition. FIGS. 5B-D show user interfaces generated by theinterface manager 460 in real time while the athlete 150 performs thesquat exercise.

FIG. 5B is a user interface 500 showing muscle activation feedbackaccording to one embodiment. The muscle activation feedback isrepresented by a depiction of muscles overlaid on an image 505 of theathlete 150, e.g., based on biofeedback generated by the biofeedbackmodule 410. In particular, the image 505 shows a metric for theathlete's right quadriceps 510 and left quadriceps 515. The metricsshown in FIG. 5B are depictions of the levels of exertion of each of thetwo muscles. The level of exertion may be represented as an activationintensity, contribution based on work, or any other metric output by thedata processing module 400. For example, the depiction of the rightquadriceps 510 is smaller than that of the left quadriceps 515 becausethe athlete is exerting with higher activation intensity on the leftquadriceps 515. The user interface 500 may include percentages alongsidethe different muscles to indicate activation intensity or contributionof the corresponding muscle to the movement. For example, thepercentages of 8% and 20% correspond to the right quadriceps 510 andleft quadriceps 515, respectively. In addition, the depiction of levelof exertion on image 505 may be shown in near real-time while theathlete is performing the exercise or may be presented as a set orworkout summary after completing the exercise. FIG. 5B also showsanother user interface 502 showing muscle activation feedback includinga depiction of muscles on the backside of the athlete, which is alsooverlaid on an image 505 of the athlete 150. For example, the userinterface 502 shows activation intensity of the athlete's hamstrings 512and glutes 514, which are also activated with the quads 510 and 515while the athlete performs a squat exercise.

FIG. 5C is a user interface 520 showing exercise balance feedbackaccording to one embodiment. The user interface 520 shows a depiction,e.g., based on biofeedback generated by the biofeedback module 410, ofthree categories 525 of exercise feedback: effort, balance, and muscles.The categories 525 shown in FIG. 5C indicate that the athlete 150 is hasa satisfactory metric (e.g., as indicated by the checkmark) for effortand muscles, but not for balance (e.g., as indicated by the X mark).Balance has an unsatisfactory metric, e.g., because the athlete 150leans too far to the left when performing squat exercises. Further, thegraph 530 indicates that the athlete 150 is “20% leaning left,” forexample, the athlete 150 is exerting approximately 20% more energy usingthe left quadriceps than using the right quadriceps, which is outside ofa target range for balance (e.g., no more than 10% based on an exerciserule in the exercise data store 440). In other embodiments, the metricfor balance may be based on other types of data such as the athlete'sacceleration of each leg when jumping and landing while performing thesquats, or the timing between muscle activation of the athlete's rightand left quadriceps (e.g., whether one quadriceps is activated slowerthan the other quadriceps when jumping).

FIG. 5D is a user interface 535 showing exercise set score feedbackaccording to one embodiment. In particular, the user interface 535 showsthe set score 540 of “8.5” generated by the biofeedback module 410. Inone embodiment, the set score 540 is point value out of a total possible10 points, where a greater point value corresponds to a higher qualityperformance. If the athlete's metric for balance was satisfactory, thenthe set score 540 would be a greater point value, e.g., “9” or “10.”

FIG. 5E is a user interface showing an exercise training programaccording to one embodiment. The exercise program builder 430 generatesthe exercise training program for an athlete, e.g., the athlete 150shown in FIG. 5A performing squats, and includes workouts scheduled on aMonday, Wednesday, and Friday of given week. On Monday, the exercisetraining program includes a warm up workout 545 and a strength workout550. On Wednesday, the exercise training program includes a cardioworkout 555. On Friday, the exercise training program includes a warm upworkout 560 and a strength workout 565. The strength workouts 550 and565 each include three sets of free squat exercises and squat exercises.Each set also include 1 minute of rest time. The free squat exercise hasten repetitions, and the squats exercise has ten repetitions with 45pounds (e.g., weight on a barbell during each squat) per repetition. Thecardio workout 555 has one set of four repetitions of a treadmill runexercise for 1 mile per repetition.

FIG. 5F is a user interface showing a modified version of the exercisetraining program shown in FIG. 5E according to one embodiment. Comparedto the exercise training program shown in FIG. 5E, the modified versionhas an additional balance workout 570 scheduled on Wednesday and amodified strength workout 565 scheduled on Friday. The balance workout570 includes four sets of alternating lunges. The alternating lungeexercise is a unilateral exercise focusing on each of the left and rightsides separately. This unilateral exercise allows the athlete to trainthe athlete's left quad, independently of the right quad, to strengthenthe left quad muscle rather than completing bilateral exercises such assquats where the athlete may be biased toward one side of muscles overthe other side. The alternating lunge exercise has five repetitions perside per set. The modified strength workout 565 includes an additionalsingle leg squat BOSU® ball exercise with ten repetitions per side(e.g., right leg and left leg).

The exercise program builder 430 generates the modified version of theexercise training program for the athlete 150 performing squats based onmetrics of performance by the athlete 150. For example, as shown in theuser interfaces in FIGS. 5B-D, the athlete 150 has an unsatisfactorymetric for balance. Thus, the exercise program builder 430 automaticallymodifies (e.g., without requiring user input) the original exercisetraining program shown in FIG. 5E by adding exercises that help developthe athlete's balance in the lower body, specifically, the alternatinglunges and the single leg squat BOSU® ball exercise.

VI. Example Use Case: Bench Press

FIG. 6A is a diagram of an athlete 150 performing a bench press exercisewhile wearing a sensor-equipped garment 130 according to one embodiment.The athlete 150 is wearing the athletic garment 130 which is a shirtincluding sensors that generate muscle activation data about theathlete's upper body muscles, e.g., pectorals, deltoids, and triceps.The athlete 150 performs a set of the bench press exercise with abarbell, e.g., a set of eight repetitions with 180 pounds of weight onthe barbell per repetition. FIGS. 6B-D show user interfaces generated bythe interface manager 460 in real time while the athlete 150 performsthe bench press exercise.

FIG. 6B is a user interface 600 showing muscle activation feedbackaccording to one embodiment. The muscle activation feedback isrepresented by a depiction of muscles overlaid on an image 605 of theathlete 150. In particular, the image 605 shows a metric for theathlete's right deltoids 610, right pectorals 615, and right triceps620. The depiction of the right deltoids 610 is larger than those of theright pectorals 615 and right triceps 620 because the athlete is using ahigher contribution of the right deltoid compared to the chest ortriceps to complete a movement of an exercise.

FIG. 6C is a user interface 625 showing target muscle feedback accordingto one embodiment. The user interface 625 shows a depiction, e.g., basedon biofeedback generated by the biofeedback module 410, of threecategories 630 of exercise feedback: effort, balance, and muscles. Thecategories 630 shown in FIG. 6C indicate that the athlete 150 has asatisfactory metric for effort and balance, but not for muscles (alsoreferred to as target muscles). The target muscles has an unsatisfactorymetric, e.g., because the athlete 150 is focusing on using the incorrecttypes of muscles for the bench press exercise. In particular, thecheckmark and X marks in the depiction of target muscles 635 indicatethat the athlete is using the deltoid muscles more than the pectoralsand triceps.

FIG. 6D is a user interface 640 showing exercise set score feedbackaccording to one embodiment. In particular, the user interface 640 showsthe set score 645 of “6.0” generated by the biofeedback module 410. Ifthe athlete's metric for target muscles was satisfactory, then the setscore 645 would be a greater value.

FIG. 6E is a user interface showing an exercise training programaccording to one embodiment. The exercise program builder 430 generatesthe exercise training program for an athlete, e.g., the athlete 150shown in FIG. 6A performing bench presses, and includes workoutsscheduled over two weeks. On the first week, the exercise trainingprogram includes a strength workout 650 on Monday and a strength workout655 on Friday. On the Monday of the second week, the exercise trainingprogram includes a strength workout 660. The strength workouts eachinclude three sets of bench press exercises and 1 minute of rest time.The number of repetitions per set of bench press exercises is eight foreach of the three strength workouts, though the weight graduallyincreases. Specifically, the weights in strength workouts 650, 655, and660 are 180 pounds, 190 pounds, and 200 pounds, respectively.

FIG. 6F is a user interface showing a modified version of the exercisetraining program shown in FIG. 6E according to one embodiment. Comparedto the exercise training program shown in FIG. 6E, the modified versionhas an additional strength workout 665 scheduled on Wednesday of thefirst week and another additional strength workout 670 scheduled onFriday of the second week. The strength workout 665 includes three setsof overhead press exercises and 1 minute of rest. Each set of overheadpress exercises has five repetitions of 85 pounds. The strength workout670 is substantially the same as the strength workouts 650, 655, and660, but with a different weight. The weights for the sets of benchpress exercises are lower relative to those shown in FIG. 6E.Specifically, the weights in strength workouts 655, 660, and 670 are 150pounds, 160 pounds, and 170 pounds, respectively.

The exercise program builder 430 generates the modified version of theexercise training program for the athlete 150 performing bench pressesbased on metrics of performance by the athlete 150. For example, asshown in the user interfaces in FIGS. 6B-D, the athlete 150 has anunsatisfactory metric for target muscles. In particular, the athlete 150has weak deltoid muscles, so the athlete's deltoids are over exertedwhen the athlete performs the bench press exercise. Thus, the exerciseprogram builder 430 automatically modifies (e.g., without requiring userinput) the original exercise training program shown in FIG. 6E by addingexercises (e.g., overhead press exercises) that help develop theathlete's deltoid muscle strength. Further, the exercise program builder430 modifies the existing strength exercises by reducing the amount ofweight per bench press exercise. Adjusting the amount of weight to asuitable level for the athlete 150 helps the athlete exercise withoutunder-exerting or over-exerting beyond the athlete's capabilities.

VII. Example Process Flows

FIG. 7 is a flowchart of a process 700 for providing exercise feedbackaccording to one embodiment. In some embodiments, the process 700 isperformed by the exercise feedback system 100—e.g., modules of theexercise feedback system 100 described with reference to FIG. 4A—withinthe system environment in FIG. 1. The process 700 may include differentor additional steps than those described in conjunction with FIG. 7 insome embodiments or perform steps in different orders than the orderdescribed in conjunction with FIG. 7.

The exercise feedback system 100 receives 710 physiological data from agarment worn by a user, e.g., the athlete 150 wearing garment 130 shownin FIG. 5A, while performing an exercise, e.g., squats. Thephysiological data is generated by sensors of the garment (e.g., sensors210-280 shown in FIG. 2) and can describe muscle activation data ofparticular muscles of the user, heart rate data, or other types of datasuch as motion data. The biofeedback module 410 compares 720 thephysiological data to reference data selected based on the exercise. Forexample, the reference data includes a target muscle data or a baselinemotion profile for squat exercises. The reference data can also be basedon previously generated physiological data of the user, e.g., duringexercises that the user performed in the past. The biofeedback module410 generates 730 biofeedback based on the comparison. The biofeedbackmay indicate a metric of performance of the exercise by the user, e.g.,whether the user performed the exercise with a satisfactory level ofeffort or balance, or using the proper form or target muscles. Theexercise feedback system 100 provides 740 the biofeedback to a mobiledevice of the user, e.g., the athlete's device 110 shown in FIG. 1. Thebiofeedback is displayed on a graphical user interface to the user. Thegraphical user interface can include a depiction of the particularmuscles of the user, e.g., the depiction of the right quadriceps 510 andleft quadriceps 520 in the user interface 500 shown in FIG. 5B. In otherembodiments, the athlete's device 110 communicates the metric ofperformance of the user in other suitable formats, e.g., as an audiofeedback via speakers of the athlete's device 110, or visual feedbackincluding text presented on a display screen of the athlete's device110.

FIG. 8 is a flowchart of a process 800 for modifying an exercisetraining program according to one embodiment. In some embodiments, theprocess 800 is used by the exercise feedback system 100—e.g., modules ofthe exercise feedback system 100 described with reference to FIG.4A—within the system environment in FIG. 1. The process 800 may includedifferent or additional steps than those described in conjunction withFIG. 8 in some embodiments or perform steps in different orders than theorder described in conjunction with FIG. 8.

The exercise program builder 430 generates 810 an exercise trainingprogram for a user, e.g., the athlete 150 shown in FIG. 6A. The exercisefeedback system 100 receives 820 physiological data from a garment wornby the user while performing an exercise of the exercise trainingprogram, e.g., the bench press exercise of a workout of the exercisetraining program shown in FIG. 6E. The biofeedback module 410 generates830 a metric of performance of the exercise by the user. For example,the metric of performance indicates that the user is not using all ofthe target muscles for the bench press exercise, as shown in FIG. 6C.The exercise program builder 430 modifies 840 the exercise trainingprogram based on the generated metric of performance. For example, theexercise program builder 430 modifies the exercise training programshown in FIG. 6E, which to help the athlete train to use all of thetarget muscles for bench press exercises. The exercise feedback system100 provides 850 information representative of the modified exercisetraining program (e.g., the user interface shown in FIG. 6F) to a mobiledevice (e.g., the athlete's device 110 shown in FIG. 1) for display tothe user. To improve the efficiency of exercise training, it isimportant for the athlete to understand whether the athlete is gainingthe intended training adaptation from the exercise or training program(e.g., improving power, strength, hypertrophy, endurance, speed, etc.)

VIII. Additional Example User Interfaces

FIG. 9A is a user interface 900 showing muscle activation feedback forupper body muscles according to one embodiment. The user interface 900shows the peak muscle activation (e.g., based on the amplitude ofphysiological data generated by sensors) for a set of muscles during aparticular set completed by an athlete. For instance, the peak muscleactivations for the left and right pectorals are 99% and 124%,respectively. In some embodiments, the peak muscle activation may be apercentage greater than 100% because the percentage is relative to abaseline calibration value for the corresponding muscle. In someembodiments, the peak muscle activation is greater when the athlete isperforming exercises with power movements, e.g., lifting heavy weightsat a high velocity.

FIG. 9B is a user interface 910 showing muscle activation feedback forlower body muscles according to one embodiment. The user interface 910indicates that the muscle contributions for the inner quads, outerquads, glutes, and hamstrings are 73%, 18%, 6%, and 3%, respectively.The biofeedback module 410 determines the muscle contributions based onthe work (i.e., energy expenditure) of the muscles over a period of time(e.g., corresponding to a set of an exercise). In one embodiment, thebiofeedback module 410 determines the muscle contributions based on theratio of work for a given muscle to the total accumulated work for a setof muscles (e.g., the lower body muscles: inner quads, outer quads,glutes, and hamstrings). The user interface 910 also indicates themuscle contributions distributed between the left and right sides ofeach muscle.

FIG. 9C is another user interface 920 showing muscle activation feedbackfor lower body muscles according to one embodiment. The user interface920 indicates that the total work exerted by the athlete's musclesduring training sessions on October 21, October 25, November 3, andNovember 4 are 346, 411, N/A (athlete did not complete a trainingsession that day), and 821. The height of each bar in the graph isproportional to the total work for the corresponding session. The userinterface 920 also indicates percentages representing the contributionof each muscle in the group of lower body muscles shown in FIG. 9C tothe total work. For instance, the contribution for the inner quads,outer quads, glutes, and hamstrings are 51%, 28%, 11%, and 10%,respectively, for the session on November 4.

FIG. 9D is a user interface 930 showing muscle contribution over timeaccording to one embodiment. In particular, the user interface 930 showsa bar graph and lines graphs on the same time axis. The bar graphindicates how much weight an athlete lifted for each set of an exerciseon a given day (e.g., session). For instance, during the session ofOctober 5, the athlete performed three sets of a squat exercise with 30pounds for each set. The overlaying line graphs indicate the percentagemuscle contributions for the outer quads and the glutes, according tothe legend of the graph. In one example use case, the outer quad musclecontribution increases and the glutes muscle contribution decreases whenthe athlete performs sets of squat exercises using greater weights, asshown in the section of the graph corresponding to the session onOctober 13. The changes in muscle contributions may help show abreakdown of proper squat exercise form at greater weights because theathlete is compensating for weaker glutes by exerting more energy usingthe outer quads. The muscle contributions may also change as result ofthe athlete's fatigue over time. Thus, the athlete or a coach may usethe biofeedback shown in the user interface 930 to modify futureworkouts, e.g., by reducing the weights for squats.

IX. Additional Considerations

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program product includinga computer-readable non-transitory medium containing computer programcode, which can be executed by a computer processor for performing anyor all of the steps, operations, or processes described.

Embodiments of the invention may also relate to a product that isproduced by a computing process described herein. Such a product mayinclude information resulting from a computing process, where theinformation is stored on a non-transitory, tangible computer readablestorage medium and may include any embodiment of a computer programproduct or other data combination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

What is claimed is:
 1. A method comprising: generating an exercisetraining program for a user including a plurality of exercises based atleast in part on user information received from the user, the exercisetraining program including a first set for performing an exercise of theplurality of exercises associated with a first load and a second set forperforming the exercise associated with a second load greater than thefirst load; establishing an interface between a garment and the user,the garment comprising a plurality of sensors, comprising one or moreelectromyography (EMG) electrodes, distributed throughout the garmentand in proximity to a plurality of muscles of the user during use, thegarment further comprising signal transmission architecture thatcommunicatively couples the plurality of sensors to a processing unit;at a processor in communication with the processing unit, receivingphysiological data from the garment worn by the user upon transmissionof one or more signal streams from the processing unit, thephysiological data describing muscle activation of one or more musclesof the plurality of muscles of the user while performing the first setand the second set; for each of the first set and the second set: at theprocessor, transforming the one or more signal streams into metrics ofperformance of the exercise by the user, wherein transforming the one ormore signal streams into metrics of performance comprises generating acontribution for each muscle of the one or more of the plurality ofmuscles associated with performing the exercise associated with therespective load, the contribution representative of a ratio between anaccumulation of muscle activation by the muscle over a period of timeand a total accumulation of muscle activation by the one or more musclesassociated with the exercise over the period of time; comparing, foreach muscle of the one or more muscles associated with the exercise, thecontribution to reference data associated with the muscle for theexercise; determining a sequence of the one or more muscles associatedwith the exercise based on timing differences between events of muscleactivation for each of the one or more muscles associated with theexercise; comparing the determined sequence to a reference sequenceassociated with the exercise; and determining a set score based on thecomparison of the contribution to the reference data and the comparisonof the determined sequence to the reference sequence; responsive todetermining that the set score associated with the first set is greaterthan the set score associated with the second set, modifying theexercise training program to include a third set associated with a thirdload less than the second load; and upon modification of the exercisetraining program, providing information representative of the modifiedexercise training program to a client device for display to the user. 2.The method of claim 1, wherein the user information describes anathletic skill that the user wants to improve by performing theplurality of exercises.
 3. The method of claim 1, wherein the userinformation describes at least one of a sport played by the user,demographic information about the user, and a history of injuries of theuser.
 4. The method of claim 1, wherein the exercise training programincludes one or more exercise workouts scheduled over a duration oftime, each exercise workout including one or more exercises of theplurality of exercises.
 5. The method of claim 4, wherein modifying theexercise training program further includes modifying at least oneexercise of the plurality of exercises.
 6. The method of claim 5,wherein each of the first set, the second set, and the third set isassociated with a weight and a number of repetitions.
 7. The method ofclaim 5, wherein modifying the exercise training program furtherincludes removing an exercise workout of one or more exercise workoutsfrom the exercise training program.
 8. The method of claim 1, whereinmodifying the exercise training program is further based on inputinformation received from at least one of the user and a coach of theuser.
 9. The method of claim 1, wherein the exercise training program ismodified based further on previously recorded physiological data of theuser.
 10. A method comprising: establishing an interface between agarment and a user, the garment comprising a plurality of sensors,comprising one or more electromyography (EMG) electrodes, distributedthroughout the garment and in proximity to a plurality of muscles of theuser during use, the garment further comprising signal transmissionarchitecture that communicatively couples the plurality of sensors to aprocessing unit; at a processor in communication with the processingunit, receiving physiological data from the garment worn by the userupon transmission of one or more signal streams from the processingunit, the physiological data describing muscle activation of one or moremuscles of the plurality of muscles of the user while performing a firstset of an exercise associated with a first load and a second set of anexercise associated with a second load; for each of the first set andthe second set: at the processor, transforming the one or more signalstreams into metrics of performance of the exercise by the user, whereintransforming the one or more signal streams into metrics of performancecomprises generating a contribution for each muscle of the one or moreof the plurality of muscles associated with performing the exerciseassociated with the respective load, the contribution representative ofa ratio between an accumulation of muscle activation by the muscle overa period of time and a total accumulation of muscle activation by theone or more muscles associated with the exercise over the period oftime; comparing, for each muscle of the one or more muscles associatedwith the exercise, the contribution to reference data associated withthe muscle for the exercise; determining a sequence of the one or moremuscles associated with the exercise based on timing differences betweenevents of muscle activation for each of the one or more musclesassociated with the exercise; comparing the determined sequence to areference sequence associated with the exercise; and determining a setscore based on the comparison of the contribution to the reference dataand the comparison of the determined sequence to the reference sequence;responsive to determining that the set score associated with the firstset is greater than the set score associated with the second set,modifying an exercise training program to include a third set associatedwith a third load less than the second load for the user; and uponmodification of the exercise training program, providing informationrepresentative of the modified exercise training program to a clientdevice for display to the user.
 11. The method of claim 10, wherein theexercise training program includes one or more exercise workoutsscheduled over a duration of time, each exercise workout including oneor more exercises of the plurality of exercises.
 12. The method of claim11, wherein modifying the exercise training program further includesmodifying at least one exercise of the plurality of exercises.
 13. Acomputer program product comprising a non-transitory computer readablestorage medium having instructions encoded thereon that, when executedby a processor, cause the processor to: generate an exercise trainingprogram for a user including a plurality of exercises based at least inpart on user information received from the user, the exercise trainingprogram including a first set for performing an exercise of theplurality of exercises associated with a first load and a second set forperforming the exercise associated with a second load greater than thefirst load; receive physiological data from a garment worn by the user,the physiological data describing muscle activation of a plurality ofmuscles of the user while performing the first set and the second set,the garment including a plurality of sensors, comprising one or moreelectromyography (EMG) electrodes, distributed throughout the garmentand in proximity to a plurality of muscles of the user during use, thegarment further comprising signal transmission architecture thatcommunicatively couples the plurality of sensors to a processing unit;for each of the first set and the second set: transform the one or moresignal streams into metrics of performance of the exercise by the user,wherein transforming the one or more signal streams into metrics ofperformance comprises generating a contribution for each muscle of oneor more of the plurality of muscles associated with performing theexercise associated with the respective load, the contributionrepresentative of a ratio between an accumulation of muscle activationby the muscle over a period of time and a total accumulation of muscleactivation by the one or more muscles associated with the exercise overthe period of time; compare, for each muscle of the one or more musclesassociated with the exercise, the contribution to reference dataassociated with the muscle for the exercise; determine a sequence of theone or more muscles associated with the exercise based on timingdifferences between events of muscle activation for each of the one ormore muscles associated with the exercise; compare the determinedsequence to a reference sequence associated with the exercise; anddetermine a set score based on the comparison of the contribution to thereference data and the comparison of the determined sequence to thereference sequence; responsive to determining that the set scoreassociated with the first set is greater than the set score associatedwith the second set, modify the exercise training program to include athird set associated with a third load less than the second load; andupon modification of the exercise training program, provide informationrepresentative of the modified exercise training program to a clientdevice for display to the user.
 14. The non-transitory computer readablestorage medium of claim 13, wherein the exercise training programincludes one or more exercise workouts scheduled over a duration oftime, each exercise workout including one or more exercises of theplurality of exercises.
 15. The non-transitory computer readable storagemedium of claim 14, wherein modifying the exercise training programfurther includes modifying at least one exercise of the plurality ofexercises.
 16. The non-transitory computer readable storage medium ofclaim 15, wherein each of the first set, the second set, and the thirdset with a weight and a number of repetitions.
 17. The non-transitorycomputer readable storage medium of claim 15, wherein modifying theexercise training program further includes removing an exercise workoutof one or more exercise workouts from the exercise training program. 18.The method of claim 1, wherein the events of muscle activation includesone or more of start of muscle activation, end of muscle activation, andpeak amplitude of muscle activation.
 19. The method of claim 6, whereina weight associated with the third set is less than a weight associatedwith the second set.
 20. The method of claim 6, wherein a number ofrepetitions associated with the third set is less than a number ofrepetitions associated with the second set.